News. Phi Delta Epsilon最新文献_第4页

Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning 使用对抗学习的工业监测替代数据增强

News. Phi Delta Epsilon Pub Date : 2022-05-09 DOI: 10.48550/arXiv.2205.04222

Silvan Mertes, A. Margraf, Steffen Geinitz, Elisabeth Andr'e

{"title":"Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning","authors":"Silvan Mertes, A. Margraf, Steffen Geinitz, Elisabeth Andr'e","doi":"10.48550/arXiv.2205.04222","DOIUrl":"https://doi.org/10.48550/arXiv.2205.04222","url":null,"abstract":"Visual inspection software has become a key factor in the manufacturing industry for quality control and process monitoring. Semantic segmentation models have gained importance since they allow for more precise examination. These models, however, require large image datasets in order to achieve a fair accuracy level. In some cases, training data is sparse or lacks of sufficient annotation, a fact that especially applies to highly specialized production environments. Data augmentation represents a common strategy to extend the dataset. Still, it only varies the image within a narrow range. In this article, a novel strategy is proposed to augment small image datasets. The approach is applied to surface monitoring of carbon fibers, a specific industry use case. We apply two different methods to create binary labels: a problem-tailored trigonometric function and a WGAN model. Afterwards, the labels are translated into color images using pix2pix and used to train a U-Net. The results suggest that the trigonometric function is superior to the WGAN model. However, a precise examination of the resulting images indicate that WGAN and image-to-image translation achieve good segmentation results and only deviate to a small degree from traditional data augmentation. In summary, this study examines an industry application of data synthesization using generative adversarial networks and explores its potential for monitoring systems of production environments. keywords{Image-to-Image Translation, Carbon Fiber, Data Augmentation, Computer Vision, Industrial Monitoring, Adversarial Learning.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"21 1","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78843002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks 基于条件生成对抗网络的情感面孔分类间标签插值

News. Phi Delta Epsilon Pub Date : 2022-04-26 DOI: 10.48550/arXiv.2204.12237

Silvan Mertes, Dominik Schiller, F. Lingenfelser, Thomas Kiderle, Valentin Kroner, Lama Diab, Elisabeth Andr'e

{"title":"Intercategorical Label Interpolation for Emotional Face Generation with Conditional Generative Adversarial Networks","authors":"Silvan Mertes, Dominik Schiller, F. Lingenfelser, Thomas Kiderle, Valentin Kroner, Lama Diab, Elisabeth Andr'e","doi":"10.48550/arXiv.2204.12237","DOIUrl":"https://doi.org/10.48550/arXiv.2204.12237","url":null,"abstract":"Generative adversarial networks offer the possibility to generate deceptively real images that are almost indistinguishable from actual photographs. Such systems however rely on the presence of large datasets to realistically replicate the corresponding domain. This is especially a problem if not only random new images are to be generated, but specific (continuous) features are to be co-modeled. A particularly important use case in emph{Human-Computer Interaction} (HCI) research is the generation of emotional images of human faces, which can be used for various use cases, such as the automatic generation of avatars. The problem hereby lies in the availability of training data. Most suitable datasets for this task rely on categorical emotion models and therefore feature only discrete annotation labels. This greatly hinders the learning and modeling of smooth transitions between displayed affective states. To overcome this challenge, we explore the potential of label interpolation to enhance networks trained on categorical datasets with the ability to generate images conditioned on continuous features.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"101 1","pages":"67-87"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89928229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliable Classification of Images by Calculating Their Credibility Using a Layer-Wise Activation Cluster Analysis of CNNs 利用cnn的分层激活聚类分析计算图像可信度的可靠分类

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-37317-6_3

Daniel Lehmann, M. Ebner

引用次数: 0

Forecast of Dengue Cases based on the Deep Learning Approach: A Case Study for a Brazilian City 基于深度学习方法的登革热病例预测:以巴西某城市为例

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011135500003277

L. S. D. Souza, S. N. Alves-Souza, L. V. L. Filgueiras, L. Velloso, M. F. Carvalho, Luciano Garcia, Marcia Ito, J. Jarske, T. L. Santos, H. Fernandes, Gabriela Araújo, Wesley Barbosa

引用次数: 0

Automatic UML Defects Detection based on Image of Diagram 基于图图像的UML缺陷自动检测

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011316900003277

Murielle Lokonon, V. R. Houndji

{"title":"Automatic UML Defects Detection based on Image of Diagram","authors":"Murielle Lokonon, V. R. Houndji","doi":"10.5220/0011316900003277","DOIUrl":"https://doi.org/10.5220/0011316900003277","url":null,"abstract":": Uniﬁed Modeling Language (UML) is a standardized modeling language used to design software systems. However, software engineering learners often have difﬁculties understanding UML and often repeat the same mistakes. Several solutions automatically correct UML diagrams. These solutions are generally restricted to the modeling tool used or need teachers’ intervention for providing exercises, answers, and other rules to consider for diagrams corrections. This paper proposes a tool that allows the automatic correction of UML diagrams by taking an image as input. The aim is to help UML practicers get automatic feedback on their diagrams regardless of how they have represented them. We have conducted our experiments on the use case diagrams. We have ﬁrst built a dataset of images of the most elements encountered in the use case diagrams. Then, based on this dataset, we have trained some machine learning models using the Detectron2 library developed by Facebook AI Research (FAIR). Finally, we have used the model with the best performances and a predeﬁned list of errors to set up a tool that can syntactically correct any use case diagram with relatively good precision. Thanks to its genericity, the use of this tool is easier and more practical than the state-of-the-art UML diagrams correction systems.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"4 1","pages":"193-198"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78420768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time Distance Measurement in a 2D Image on Hardware with Limited Resources for Low-power IoT Devices (Radar Control System) 低功耗物联网设备(雷达控制系统)在有限资源硬件上的二维图像实时距离测量

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011188100003277

Jurij Kuzmic, G. Rudolph

引用次数: 0

Active Collection of Well-Being and Health Data in Mobile Devices 在移动设备中积极收集幸福和健康数据

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-37317-6_2

João Marques, Francisco Faria, Rita Machado, Heitor Cardoso, Alexandre Bernardino, Plinio Moreno

引用次数: 0

Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach 使用完全基于深度学习的方法创建自动道路标志库存系统

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011266100003277

Gabriele Galatolo, Matteo Papi, Andrea Spinelli, Guglielmo Giomi, A. Zedda, M. Calderisi

{"title":"Creating an Automatic Road Sign Inventory System using a Fully Deep Learning-based Approach","authors":"Gabriele Galatolo, Matteo Papi, Andrea Spinelli, Guglielmo Giomi, A. Zedda, M. Calderisi","doi":"10.5220/0011266100003277","DOIUrl":"https://doi.org/10.5220/0011266100003277","url":null,"abstract":": Some road sections are a veritable forest of road signs: just think how many indications you can come across on an urban or extra-urban route, near a construction site or a road diversion. The automatic recognition of vertical traffic signs is an extremely useful task in the automotive industry for many practical applications, such as supporting the driver while driving with an in-car advisory system or the creation of a register of signals for a particular road section to speed up maintenance and replacement of installations. Recent developments in deep learning have brought huge progress in the image processing area, which triggered successful applications like traffic sign recognition (TSR). The TSR is a specific image processing task in which real traffic scenes (images or frames from videos taken from vehicle cameras in uncontrolled lighting and occlusion conditions) are processed in order to detect and recognize traffic signs within it. Traffic Sign Recognition is a very recent technology facilitated by the Vienna Convention on Road Signs and Signals of 1968: during that international meeting, it was decided to standardize traffic signs so that they could be recognised more easily abroad. Finally, this work summarizes our proposal of a practical pipeline for the development of an automatic traffic sign recognition software.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"10 2 1","pages":"102-109"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78978222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Faster Converging Negative Sampling for the Graph Embedding Process in Community Detection and Link Prediction Tasks 社区检测和链路预测任务中更快收敛的负抽样图嵌入过程

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011142000003277

K. Loumponias, Andreas Kosmatopoulos, T. Tsikrika, S. Vrochidis, Y. Kompatsiaris

引用次数: 0

Evaluating and Improving RoSELS for Road Surface Extraction from 3D Automotive LiDAR Point Cloud Sequences 基于三维汽车激光雷达点云序列的道路表面提取方法的评价与改进

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-37317-6_6

Dhvani Katkoria, Jaya Sreevalsan-Nair

引用次数: 0